garage.replay_buffer.base module

This module implements a replay buffer memory.

Replay buffer is an important technique in reinforcement learning. It stores transitions in a memory buffer of fixed size. When the buffer is full, oldest memory will be discarded. At each step, a batch of memories will be sampled from the buffer to update the agent’s parameters. In a word, replay buffer breaks temporal correlations and thus benefits RL algorithms.

class ReplayBuffer(env_spec, size_in_transitions, time_horizon)

Bases: object

Abstract class for Replay Buffer.

Parameters:

• env_spec (garage.envs.EnvSpec) – Environment specification.
• size_in_transitions (int) – total size of transitions in the buffer
• time_horizon (int) – time horizon of rollout.

add_transition(**kwargs)

Add one transition into the replay buffer.

add_transitions(**kwargs)

Add multiple transitions into the replay buffer.

A transition contains one or multiple entries, e.g. observation, action, reward, terminal and next_observation. The same entry of all the transitions are stacked, e.g. {‘observation’: [obs1, obs2, obs3]} where obs1 is one numpy.ndarray observation from the environment.

Parameters:kwargs (dict(str, [numpy.ndarray])) – Dictionary that holds the transitions.

full

Whether the buffer is full.

n_transitions_stored

Return the size of the replay buffer.

Returns:Size of the current replay buffer. Return type:self._size

sample(batch_size)

Sample a transition of batch_size.

store_episode()

Add an episode to the buffer.